Sr Linux Networking Engineer

You are a seasoned networking engineer who has designed, deployed, and operated high-performance networks at scale. At fal, our platform orchestrates AI inference workloads across thousands of GPUs spread over multiple data centers and cloud providers. You will own the network layer that ties it all together—ensuring that model traffic, storage I/O, and control-plane communication are fast, reliable, and secure. You think in terms of packets per second, tail latency, and fabric utilization, and you automate everything you touch.

• Design, build, and operate the network fabric that interconnects our GPU fleet, including spine-leaf architectures, RDMA/RoCEv2 networks for distributed inference, and overlay networks for tenant isolation.
• Own L2/L3 network design across bare-metal and cloud environments, including BGP peering, ECMP, VXLAN/EVPN, and high-bandwidth interconnects between data centers.
• Develop and maintain network automation using Ansible, Terraform, and custom tooling to provision, configure, and validate switches, routers, DPUs, and SmartNICs at scale.
• Instrument deep network observability—build dashboards, alerting, and anomaly detection across our fabric using Prometheus, Grafana, and packet-level telemetry.
• Partner with the Compute and ML Performance teams to tune network paths for AI workloads, minimizing latency for model serving and maximizing throughput for large tensor transfers.
• Drive incident response and root-cause analysis for network-related production issues and build automation to prevent recurrence.
• Evaluate and qualify new networking hardware and software—NICs, switches, DPUs, SONiC, Cumulus, and similar—as we scale to next-generation GPU clusters.

• 8+ years of experience building and operating large-scale networks, ideally in GPU cloud, HPC, or hyperscale environments.
• Deep expertise in Linux networking internals: kernel networking stack, iptables/nftables, tc, eBPF, network namespaces, bonding/teaming, and SR-IOV.
• Strong command of routing and switching protocols: BGP, OSPF, ECMP, VXLAN, EVPN, MPLS, and segment routing.
• Hands-on experience with high-performance networking for AI/ML: RDMA, RoCEv2, InfiniBand, GPUDirect, and NCCL tuning.
• Proficiency automating network infrastructure with Ansible, Python, Go, and Git.
• Experience with network-as-code workflows.
• Familiarity with modern network operating systems such as SONiC, Cumulus Linux, Arista EOS, or Nokia SR Linux.
• Experience with network observability stacks: Prometheus, Grafana, sFlow/NetFlow, and packet capture tools.

• Experience with DPU/SmartNIC programming (NVIDIA BlueField, AMD Pensando) and SDN/NFV architectures.
• Contributions to open-source networking projects (SONiC, FRR, DPDK, eBPF/XDP).
• Experience operating networks that support Kubernetes and container-native workloads (Calico, Cilium, MetalLB).
• Familiarity with data center physical layer design, optics, and cabling at scale